Coastal Ocean Observation Lab

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Coastal Ocean Modeling, Observation, and
Prediction Program Exploring the Limits of
Predictability in the Coastal Interface
John Wilkin, Hernan Arango, Julia Levin,Javier
Zavala-Garay, Gordon Zhang Regional Ocean
Prediction Scott Glenn, Oscar Schofield, Bob
Chant, Josh Kohut Coastal Ocean Observation
Lab Janice McDonnell Education and Outreach
Regional Ocean Prediction http//marine.rutgers.e
du/po
Coastal Ocean Observation Lab http//marine.rutger
s.edu/cool
Education Outreach http//coolclassroom.org
Coastal Observation and Prediction Sponsors
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Rutgers University Coastal Ocean Observation Lab
Corporate Partners
11) Public Service Electric Gas 12) NorthWest
Research Associates 13) Metron 14) SAIC 15)
Oceantemp 16) Jenifer Clarks Gulf Stream 17)
Weatherflow 18) Applied Science Associates 19)
Seatow 20) Millers Launch 21) Eagle Aire 22)
AirNet Broadband
1) CODAR Ocean Sensors 2) Webb Research
Corporation 3) SeaSpace 4) Satlantic 5) WetSat 6)
Aanderaa 7) RD Instruments 8) Nortek 9)
Otronix 10) Ocean Power Technologies
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Rutgers University Coastal Ocean Observation
Lab Operations Center
Ship-to-Shore Communications
CODAR Network
Cable
Glider Fleet
X-Band
L-Band
Mission Sustained Operations of Key Observing
Technologies for Scientific Research, Technology
Development, Education and Outreach
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Shallow Water 2006 WRF Weather Forecasting Daily
Cycles
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LEAR
NLIWI
AWACS
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Research Vessels Knorr (WHOI) Endeavor
(URI) Oceanus (WHOI) Sharp (UDel) Quest
(Canada) Tioga (WHOI) Rutgers small vessels NOAA
vessel July 15 September 15
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SW06 Morning Report Statistics June 16 ---
Sept 18 Morning Reports 78 Storm Alerts
10 Locations 5 Wayport WiFi 94 Hrs
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NOAA National Hurricane Center http//www.nhc.noaa
.gov/ Tropical Storm Ernesto
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Tropical Storm Ernesto
NOAA Hydrological Prediction Center (HPC)
Satellite Surface Analysis 09/01 1815 GMT
NOAA National Hurricane Center (NHC) Ernesto
Forecast Cone 09/01 0500 EDT
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Not an Official Storm Product. Use for Scientific
Research Purposes Only.
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Real-time Forecast RU-WRF 10km
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Daily Reports Special Events
Tropical Storm Ernesto
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Tropical Storm Ernesto Feedback to the State
Accumulated Rainfall
Predicted Observed
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Damage to the Corn Crop
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Tropical Storm Ernesto Real-time Model Validation
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Tropical Storm Ernesto September 2, 2006 1300
GMT
WRF Forecast Surface Winds
CODAR Observations Surface Currents
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Tropical Storm Ernesto September 1, 2006 1900
GMT
WRF Forecast Surface Winds
CODAR Observations Surface Currents
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Tropical Storm Ernesto September 2, 2006 0700
GMT
WRF Forecast Surface Winds
CODAR Observations Surface Currents
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Tropical Storm Ernesto September 2, 2006 1900
GMT
WRF Forecast Surface Winds
CODAR Observations Surface Currents
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Tropical Storm Ernesto September 3, 2006 0100
GMT
WRF Forecast Surface Winds
CODAR Observations Surface Currents
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Base Case Date August 31, 2006 1800
GMT Boundary Conditions NCEP GFS Resolution
18km SST NCEP RTG_SST_HR 1/12 Physics RUWRF
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Hurricane Landfalls
Hurricane Tracks Through New Jersey
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Summary Conclusions Tropical Storm Ernesto
RU-WRF provided the best real-time forecast of
Tropical Storm Ernesto after landfall. Used by
Researchers, by Regional, State Local Managers,
by Power Companies, by Agriculture
Extension. The most significant difference with
operational models was improved physics. This is
a common storm track for the Mid Atlantic.
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Summary Conclusions Ocean Observatories
Real Time Integrated Ocean Observing Systems with
Linked Forecast Models are Possible
Today! Rutgers Observatory Operated in the NOPP
Tradition Of Government, Academic, Industry
Partnerships Funded by the Users - Includes
Research and Applied Benefits are Widespread

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Tropical Storm Ernesto Sub-Surface Impacts
Before
June 14, 2006 - Present
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Tropical Storm Ernesto Sub-Surface Impacts
During
June 14, 2006 - Present
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Tropical Storm Ernesto Sub-Surface Impacts
After
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Model Sensitivity Matrix
Run 1 2 3 4 5 6 7 8 9 10 11 12
Res. 18 km 18 km 37 km 37 km 100 km 100 km 18 km 18 km 37 km 37 km 100 km 100 km
SST High 1/12 Low 1/2 High 1/12 Low 1/2 High 1/12 Low 1/2 High 1/12 Low 1/2 High 1/12 Low 1/2 High 1/12 Low 1/2
Phys WRF WRF WRF WRF WRF WRF GFS GFS GFS GFS GFS GFS
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RU-WRF Physics

• Scheme used for RU-WRF model since late 2005,
  implemented after several months of operational
  trial and error.
• CU_PHYSICS Betts-Miller-Janjic scheme
  Adjustment scheme for deep and shallow convection
  relaxing towards variable temperature and
  humidity profiles determined from thermodynamic
  considerations (Janjic 1994, 2000).
• MP_PHYSICS WSM Single-Moment 5-class scheme A
  slightly more sophisticated version of (3) that
  allows for mixed-phase processes and super-cooled
  water. Also from Hong, Dudhia and Chen (2004).
  Used for real-time runs.
• BL_PBL_PHYSICS Mellor-Yamada-Janjic scheme The
  NAM operational scheme. One-dimensional
  prognostic turbulent kinetic energy scheme with
  local vertical mixing (Janjic 1990, 1996a, 2002).
  
• SF_SFCLAY_PHYSICS Janjic Similarity Used in
  Eta/NAM model. Based on Monin-Obukhov with
  Zilitinkevich thermal roughness length and
  standard similarity functions from look-up
  tables. (Janjic 1996b Chen et al. 1997).
• SF_SURFACE PHYSICS Noah Land Surface Model
  Unified NCEP/NCAR/AFWA scheme with soil
  temperature and moisture in four layers,
  fractional snow cover, and frozen soil physics.
  (Chen and Dudhia, 2001).

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WRF-GFS Physics

• Physics schemes were implemented for this
  sensitivity study in attempt to match as closely
  as possible operational NCEP models. Operational
  GFS Cumulus and Microphysics options not yet
  added to WRF-ARW option, so operational NAM
  options used instead.
• CU_PHYSICS New NAM Kain-Fritsch scheme A deep
  and shallow sub-grid scheme using a mass flux
  approach with downdrafts and CAPE removal time
  scale. (Kain 2004, Kain and Fritsch 1990, 1993).
• MP_PHYSICS New Ferrier The operational
  microphysics in NAM model. An efficient scheme
  with prognostic mixed-phase processes. The scheme
  was recently so that ice saturation is assumed at
  temperatures less than -30C rather than -10C as
  in the original implementation.
• BL_PBL_PHYSICS NCEP Global Forecast System (GFS)
  scheme First-order vertical diffusion of Troen
  and Mahrt (1986) and further described by Hong
  and Pan (1996). The PBL height is determined by
  an iterative bulk-richardson approach working
  from the ground upward whereupon the profile of
  the diffusivity coefficient is specified as a
  cubic function of height. Coefficient values are
  obtained by matching the surface-layer fluxes. A
  counter-gradient flux parameterization is
  included.
• SF_SFCLAY_PHYSICS NCEP Global Forecast Systems
  (GFS) Scheme The Monin-Obukhov similarity
  profile relationship is applied to obtain the
  surface stress and latent heat fluxes using a
  formulation based on Miyakoda and Sirutis (1986)
  modified for very stable and unstable conditions.
  The land surface evaporation has three
  components, direct evaporation from soil and
  canopy, and transpiration from vegetation (Pan
  and Mahrt 1987).
• SF_SURFACE PHYSICS Noah Land Surface Model
  Unified NCEP/NCAR/AFWA scheme with soil
  temperature and moisture in four layers,
  fractional snow cover, and frozen soil physics.
  (Chen and Dudhia, 2001).